What explains U.S. Covid-19 Vaccination Rates? A Machine Learning Workflow for Ecological Inference
1 Introduction
What explains variation in COVID-19 vaccination uptake across U.S. counties? By mid-2021, only 60% of Americans have been partially vaccinated against COVID-19. Vaccination uptake varies widely between counties (percentiles 10th=32%; 25th=37%; 50th=44%; 75th=52%; 90th=60%; entropy=3.81 bits), and within and between states (Figure 1). Currently, there is a surplus of over 60 million vaccine doses that have been delivered but not yet administered (CDC 2020b). As of this writing, there have been at least 600 thousand COVID-19 deaths, cases are increasing again nationally, and forecast models expect thousands deaths in the coming weeks (CDC 2020a). Every percent increase in vaccine uptake has the potential to prevent thousands of deaths, tens of thousands of hospitalizations, and hundreds of thousands of infections (Bartsch et al. 2021). Understanding the mismatch between available vaccine doses and unvaccinated communities is therefore an immediate health and welfare priority.
A caption
This article investigates possible data generating processes behind variation in U.S. vaccine uptake across counties. Our intended contributions are as follows: (1) A standardized benchmark for COVID-19 uptake that can be used to compare the performance of ours and other models; (2) A standardized cross-validation strategy for evaluating performance out of sample; (3) A synthesis of the existing literature on vaccine uptake into plausible data generating processes; (4) A high performing model that explains the majority of variation in vaccine uptake in the U.S.; and (5) Extensive ablation analysis that precisely highlights the importance of subsets of features.
The article is organized into the following sectoins. The first section provides a brief literature review of comparable vaccine uptake projects for COVID-19 in the US. The next section defines the outcome, unit of analysis, and domain. We then describe out measurement strategy for vaccine uptake, and propose a train, validation, and test strategy.
2 Current State of the Art
We organize the research on vaccine uptake along the following lines. In this section we summarize only the most proximate literature on explaining COVID-19 vaccine uptake across U.S. geographic units, listed in Table 1. In the next section discussing our outcome, we review relevant measurement projects attempting to record uptake accurately. Finally in the section theorizing possible data generating processes for vaccine uptake we summarize the extensive literature on vaccine hesitancy, vaccine supply, previous vaccnie campaigns, etc.
| Cite | Unit | Hold Out | Source | Reported Performance | Features | Functional Form |
|---|---|---|---|---|---|---|
| Chernyavskiy, Richardson, and Ratcliffe (2021) | County-Week | 1,2,4,8 week rolling future | CovidActNow | MAE 3.82%, 5.98%, 9.45%, 16.05% | education, race, age, covid, population | Bayesian Autoregressive Spatial Beta Linear Model |
| Mishra et al. (2021) | County | None | CDC | R^2=0.17 | historic under-vaccination, sociodemographic barriers, resource constrained healthcare, healthcare accessibility, irregular care-seeking | Multi-level linear model |
| Stewart et al. (2021) | County | None | Covidcast | None | COVID-19 Community Vulnerability Index | Multilevel Linear Model (Population Weighted) |
| Pathak, Menard, and Garcia (2021) | NA | NA | NA | NA | NA | NA |
The state of the art for explaining COVID-19 vaccine uptake across geographic units in the U.S. is poorly defined and incomplete. There is no accepted standard benchmark for comparing performance of competing explanations, each study picks their own subset of untis, time cut off, and data source on an ad hoc basis. With the exception of a very small literature attempting to forecast vaccine rates, this work is largely exploratory, focusing on whether a simple model places non-zero weight on a feature and reporting only in sample performance or ignoring performance all together.
Forecasting work projects known uptake rates some number of weeks into the future (Chernyavskiy, Richardson, and Ratcliffe 2021). They focus on a different question, what explains change in vaccine uptake over time, and so predict future values conditional not just on features but also past known values, whereas we seek to understand what previous features prior to the start why the entire vaccine roll out was more successful in some parts of the country than others.
(Mishra et al. 2021), predicts county level vaccine uptake using CDC data, with a multi-level linear model, fit to a hand engineered ranking indexes of 28 county measures organized into 5 themes (historic under-vaccination, sociodemographic barriers, resource-constrained healthcare system, healthcare accessibility barriers, and irregular care-seeking behavior). Their best performing model has an marginal \(R^2\) of only 0.17, which while not directly comparable does illustrate the low starting baseline for accounting for uptake variation in the existing literature. (Stewart et al. 2021) fit multilevel models of uptake measured by COVIDcast weighted and find nonzero weights placed on COVID-19 Community Vulnerability Index but do not report performance.
The next most direct work is descriptive
3 Vaccine Uptake
3.1 Outcome, Performance Metric, Unit of Analysis, and Domain
Our outcome (\(VaccineUptake\)) is the number of persons at least partially vaccinated in each county (\(Vaccinated\)) divided by the over 18 population measured by the recent 2020 census (\(Pop18+\)). We choose this specific outcome instead of nearby alternatives such as fully vaccinated, or percent of eligible population, for several reasons. First, our substantive practical interest is in why some Americans who are not currently vaccinated might become vaccinated in the near future. Those that receive one dose are likely to receive the second, those that don’t are at least have partially immunity, and for measurement purposes some vaccines such as Johnson & Johnson only require one dose and constitute fully vaccinated. Second, eligibility criteria is endogenous to uptake across groups first in line and in any case nearly uniform across states now. We choose the denominator of 18 plus population because it is much more accurately measured in the recent 2020 census release than 12 plus population based on extrapolations from the 2010 census and because vaccination for children 12 to 18 is still much more uncommon in the U.S.
We choose out of sample root mean squared error (RMSE) as our performance metric \(L(\hat{VaccineUptake_{oob}}, VaccineUptake_{oob})\).
We limit our domain to the continental United States, excluding Alaska, Hawaii, and island territories because of their unique logistical constraints in vaccine distribution. Our unit of analysis is the U.S. county at a single time point, July 1, 2021. Counties are the smallest dissagregation available for the entire U.S. as zip-code level data is available for a few states, and cross-unit measurement error encountered already at the county level make us dubious about zooming in with current data.
3.2 Measurement Strategy
Our measurement strategy is based on official statistics compiled by county and state health departments, further aggregated into a national panel by third parties. We consider four national panels compiled by the CDC (“COVID-19 Vaccinations in the United States,County | Data | Centers for Disease Control and Prevention,” n.d.), Vaccinetracking.us (“Data,” n.d.), CovidActNow (“Data Definitions | Covid Act Now,” n.d.), and the USA Today News Network. For a random sample of counties and a number outliers, we compared these national panels to direct reporting on state and county websites. We concluded that there are sources of measurement error (see Appendix 1), that necessitate aggregating across panels, taking the mean uptake for each county (\(c\)) reported, \(Y_c=mean_c(Vaccinetracking.us_c, CovidActNow_c)\). We use \(USAToday_c\) which tracks only completed vaccination as a check for outliers, and we exclude \(CDC_c\) entirely do to extraordinary missingness and underreporting.
There are at least three main measurement error concerns that we are aware of. The first is that states/counties either fail to report or national panels fail to pull correct counts for a non-random subset of states and counties. This error was most pronounced in the CDC panel and make it inappropriate for this kind of analysis, despite being the most relied upon source in the literature. Second, there is reporting error by state that fails to correctly record home county of the individual, either missing the information entirely or incorrectly attributing it to the county where the vaccine was administered. Of the panels, only CovidActNow explicitly attempts to correct for entirely missing county records due to policy in a handful of states. Together, we find the biggest threat to measurement to come from under-reporting rather than over-reporting. The most likely possible negative consequences of our decision to take the maximum across sources is to attenuate variation between neighboring counties and to replace ad-hoc measurement failure with ad-hoc imputation by CovidActNow for a handful of states. We find both risks greatly preferable to the known problems with the data as is, and we mitigate them in our modeling strategy. The third is county data do not consistently take into account doses administered through federal sources, e.g. Department of Defense, Veterans Affairs, Homeland Security, etc. Here too we attempt to mitigate this with features that measure military and veteran presence as well as in the case of the VA, counts of doses administered by installation in a given county.
3.3 Train, Validation, and Test Strategy
Our goal is to decompose observed vaccine uptake into an unmeasured error component and a measured data generating processes orthogonal to the error component whose contribution comes only through the features. We further want to evaluate many possible data generating processes. These goals are challenging for observational social science data and require specific inferential strategies.
One challenge is that our data are a one-time collection, we cannot inductively form hypotheses and then requisition a new batch of data from the same data generating process (DGP) to evaluate them. We therefore need to partition the data in hand into a training split where we fit models, a validation split where we refine those models inductively, and a final test split where we evaluate our final decisions and ideas. Our confidence in a possible DGP being the true one is in how well it generalizes to new unseen draws from the same process. From an information theoretic perspective, we desire a compact representation that removes idiosyncratic information leaving only information that generalizes to any draws from the DGP. This is in contrast to the typical practice in the social sciences of making strong theoretical assumptions about the DGP and then providing evidence in the form a nonzero weight placed on a feature by a model fit to in sample data. That practice is inappropriate here because we desire to explain variation in vaccine uptake, we don’t have strong prior beliefs on the role of any single feature, a non zero weights assigned by a model is not strong evidence that a particular feature is important (Bzdok, Engemann, and Thirion 2020), repeated testing of the same in sample data quickly diminishes information learned by each of that kind of test (Thompson et al. 2020), considering more than a few features quickly diminishes the intended interpretation of those weights (Achen 2005), and any sufficiently flexible functional form makes simply memorizing a dataset trivial.
A second challenge is that our data are not independent and identically distributed (IID) draws from the same DGP, which reduces the amount of unique information available and makes selection of splits difficult (Roberts et al. 2017). Our county observations are administratively correlated at the state level at a minimum through vaccine distribution strategies and data reporting mechanisms. Our county observations are at a minimum spatially correlated through transportation logistics, flow of vaccine seekers from one county to another, and reporting error assigning some vaccinations to the location it was administered and not the home address of the person. Training on a county and then testing on its immediate neighbor may end up memorizing local geographic patterns rather than the actual role of features.
Our train, validation, and test strategy is based on consensus features within nested cross-validation (Parvandeh et al. 2020). We split our county data along state lines into 5 large geographically contiguous regions shown in Figure 1. We choose regions by hierarchically clustering states based on total human interstate travel between each pair of states in 2019 measured by cell phone locations collected by SafeGraph (Kang et al. 2020).1 An outer loop withholds a region as a test set which is only used for making out of sample predictions, never training. An inner loop fits a model 4 times in 4 folds cross-validation, each iteration training on 3 regions and predicting on 1 validation region. This cross-validation step allows us to choose model hyper-parameters, most important of which is which features to include in the final model.
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Our feature selection strategy proceeds in three steps. First, our modelling technology is gradient boosted trees (GTB) which is a greedy algorithm that iteratively includes features until a condition is met. This immediately prunes most features that are never selected in any of the folds. Second, we subset to consensus features that are chosen in at least 2 of the 4 CV folds further reducing to just features that found broad geographic support. Our final step is to drop features which do not improve out of sample CV performance. Directly checking the performance of every subset is intractable (\(k!\) many possible subsets), and so we rank order features by suspected importance and test the \(k\) number of cumulative sets. Our measure of importance is the LossSHAP value which is the change in residuals on the hold out validation set when we subtract off the marginal contribution (SHAP value) of each feature (Lundberg et al. 2020). This criteria is most directly relevant to the outcome we care about, performance on unseen out of sample data, and allows for the possibility of negative importance where a feature leads to over-fitting and doesn’t generalize.
We then finalize our feature and hyper parameter selection, fit a single model to the full data of all 4 training folds, and make a single out of sample prediction on the remaining 1 test fold. By doing this full procedure separately for each of the 5 folds, we produce full out of bag predictions for every county in the U.S. The results can be directly interpreted as the amount of out of sample performance in predicting vaccine uptake that can be purchased with a given set of features and fixed regularization budget. How useful a feature is can be interpreted directly in terms of how much unique performance it buys, and its role in the DGP can be interrogated via the functional form chosen across all of the individual models.
3.4 Identification Strategy, Placebo Comparisons, and Feature Evaluation
Our research design is purely observational, we make no claim to exogeneity, nor do we believe that even our large number of features ‘control’ for relevant confounding. Instead, our strategy is an information theoretic one, to propose and rigorously evaluate compact representations that potentially generalize to more draws from the same data generating process. We supplement this with placebo analysis to determine to what degree a representation is uniquely good for our outcome of vaccine uptake relative to other similar but unrelated outcomes (Eggers, Tuñón, and Dafoe 2021). We therefore consider a feature ‘important’ if it meets the following criteria (1) it greatly improves the ability to predict the outcome in out of sample observations (2) that improvement is unique to that feature, and cannot be easily reconstruct through other features that do not share the same theoretical interpretation, and (3) that improvement is unique to our outcome. Features that meet all three criteria suggest further research using either more appropriate individual level data or an experimental design with plausible causal identification. Features that do not may still be part of the true data generating processes, but were not distinguished in the county level data available here.
4 Potential Data Generating Processes of Vaccine Uptake
4.1 Historical Background
COVID-19 vaccinations began in the U.S. on December 14, 2020 (Affairs (ASPA) 2020). The U.S. Food and Drug Administration issued an emergency use authorization (EUA) for persons 16 years or older in December, 2020 which it expanded to 12 and older in May, 2021. The majority of vaccinations given in the U.S. are the two dose sequence by Pfizer, followed by Moderna, and then to a much smaller degree the single dose Johnson and Johnson which was briefly paused in April, 2021. The number of vaccinations given per day increased nearly monotonically before peaking nationally in mid-April at over 3 million doses per day, and then declining to a nadir of about half a million doses per day in July. With the advent of the SARS-CoV-2 Delta variant and a fourth wave of cases in the U.S. vaccination rates are beginning to increase again albeit much more slowly.
4.2 Potential Data Generating Process and Feature Proposals
4.3 Supply Side
Before May 10, 2021 vaccines were allocated in a tiered system allocating lots across states (based on their total adult population) and federal agencies, who in turn chose health departments, hospitals, and retail pharmacies (“COVID-19 Vaccine Allocations,” n.d.). After May 10, individual locations could order vaccines directly from the supplier. States issued tiered eligibility schedules that prioritized elderly, healthcare workers, essential workers, etc. States and cities further prioritized demographic and economic groups sometimes at the leve of individual zip codes (Schmidt et al. 2021).
We consider a data generating process for reported vaccinations that is a function of institutional reporting mechanisms, vaccine supply, and vaccine demand. Vaccine demand is defined here as the number of vaccines that would be administered given unconstrained supply. When supply is constrained in any way, or imperfectly measured, then demand is only partially observed to be at least as much as the given supply but possibly much higher. Likewise, vaccine supply is defined here as the number of vaccines that would be administered given unconstrained demand. With perfect demand and measurement, any unused vaccines provide an upper bound on possible demand. Institutional reporting mechanisms are the process by which actual vaccine uptake is mapped into publicly reported records. As demonstrated above, institutional measurement error of health outcomes has systematic nonrandom sources of error. In some cases those systematic components may be an even larger part of the data generating process than the underlying empirical data generating process we actually care about.
Age elgibility leads to major differences (Pathak, Menard, and Garcia 2021)
idiosyncratic state policies religious exemption https://www.tennessean.com/story/news/politics/2021/03/31/covid-19-vaccination-religious-exemptions-during-public-health-crisis-advance-senate/4825721001/ Parental exemption for teenagers https://www.tennessean.com/story/news/health/2021/07/12/tennessee-fires-top-vaccine-official-covid-19-shows-new-spread/7928699002/
VaxMap 2.0 Number of facilities and driving distance to a facility (n.d.)
4.4 Demmand Side
Vaccinations per day increased week on week, peaking to average of 3 million per day in early April, and now tapering to about half a million per day.
The most proximate measure of demand available is survey self reported desire to receive a vaccine. The COVID-19 Trends and Impact Survey (CTIS) run by the Delphi group at Carnegie Mellon in partnership with Facebook
(Barkay et al. 2020) We propose to measure demand most directly with survey questions of self reported desire for a vaccination.
Predictors of willingness to get a COVID-19 vaccine in the U.S https://bmcinfectdis.biomedcentral.com/articles/10.1186/s12879-021-06023-9 (Kelly et al. 2021)
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7993557/ (Hughes et al. 2021)
social vulnerability (Hughes et al. 2021)
https://aspe.hhs.gov/pdf-report/vaccine-hesitancy
The most proximate measure of demand available is survey self reported desire to receive a vaccine. COVID-19 Trends and Impact Survey (CTIS) run by the Delphi group at Carnegie Mellon (Barkay et al. 2020) We propose to measure demand most directly with survey questions of self reported desire for a vaccination.
typically referred to as vaccine hesitancy,
(Truong et al. 2021)
(Pitts and Freeman 2021)
https://www.va.gov/vetdata/veteran_population.asp Vertans per county https://www.va.gov/vetdata/veteran_population.asp
(Brown, Young, and Pro 2021)
Large scale cross-national polling of vaccine confidence (Figueiredo et al. 2020).
Same features also predict deaths (Ruck, Bentley, and Borycz 2021)
features continue to be important at the subcounty neighborhood level (Lei 2021)
Download Options Covid-19 Vaccination Provider Locations in the United States
5 Results
We start broadly, considering the role of large non-mutually exclusive categories of features that share an intended substantive concept. We provide three views on the importance of each category of features. In each case, we evaluate features in terms of their impact on out of sample performance, reported here as RMSE and annotated with percent reduction in error from a null intercept only baseline, which has a RMSE of 11.3% and a MEA of 9%. For example, our best performing model/set of features has a RMSE of 6.74 which is a -40.5% reduction in predictive error from the baseline.
Our first measure of feature importance is predictive power of a model with access to only features from that category. Here, self reported attitudes towards global warming and solutions towards global warming provide the most direct information (RMSE 8.11, -28.4%). Alone, categories of features vary meaningfully in how much information they provide from a great deal, voting and masking behavior, to hardly any family structure vaccine supply. Our second measure of importance is how much unique information a category of features provides, measured as the change in predictive power of a model with access to every feature but those from that category. Here, model performance degrades the most when voting behavior is withheld (RMSE 7.34, -35.2%). Unlike before, variation in the importance across features is much weaker with many features providing redundant information that can be used to reconstruct the others, a difference in only -4.4% reduction in error from best to worst performance. While global warming beliefs held the most direct information, witholding them and keep all others barely impacts the model, as it can be reconstructed from other features like education and voting behavior. Lastly, we rank order categories of features from most unique to least unique information and cumulatively remove least unique (redundant) features. We find that not only does model performance not degrade, it improves, peaking when we exclude 6 categories of features, marriage, foreign born, covid, global warming, health conditions, and citizenship. Removing access to these features improves model fit, reduces over-fitting, and removing any more than these starts to degrade model performance.
| Features | Only this Category | All but this Category | Cumulatively Remove Categories | ||||
|---|---|---|---|---|---|---|---|
| Category | Count | RMSE | Δ vs. Null | RMSE | Δ vs. All | RMSE | Δ vs. Null |
| race | 8055 | 8.72 | -23.0% | 7.27 | 1.3% | ||
| google_mobility | 3 | 9.58 | -15.4% | 7.2 | 0.4% | ||
| longer_term_mobility | 761 | 7.2 | 0.3% | ||||
| voting | 6 | 8.84 | -22.0% | 7.19 | 0.3% | ||
| masking | 5 | 9.51 | -16.1% | 7.19 | 0.3% | ||
| religion | 154 | 8.97 | -20.8% | 7.17 | 0.0% | ||
| withold_nothing | 20133 | 7.05 | -37.8% | 7.17 | 0.0% | ||
| family_structure | 2143 | 11.1 | -2.0% | 7.14 | -0.4% | ||
| vaccine_supply | 7 | 11.2 | -1.0% | 7.1 | -1.0% | ||
| marriage | 1503 | 7.09 | -1.1% | ||||
| population | 7621 | 10.1 | -10.7% | 7.09 | -1.1% | ||
| vaccine_demand | 5 | 9.51 | -16.0% | 7.09 | -1.1% | ||
| insurance | 861 | 11 | -2.9% | 7.09 | -1.2% | ||
| covid | 4 | 10.2 | -9.8% | 7.08 | -1.3% | ||
| mobility_safegraph | 6 | 9.71 | -14.3% | 7.08 | -1.3% | ||
| healthcare_services | 7 | 9.67 | -14.6% | 7.07 | -1.5% | ||
| foreign_born | 1316 | 10.8 | -4.4% | 7.07 | -1.5% | ||
| health_conditions | 16 | 9.48 | -16.3% | 7.04 | -1.8% | ||
| housing | 2955 | 8.89 | -21.6% | 7.03 | -1.9% | ||
| households | 5090 | 8.41 | -25.8% | 7.03 | -2.0% | 7.06 | -37.7% |
| disabilities | 561 | 7.03 | -2.0% | 6.97 | -38.5% | ||
| amenities | 404 | 7.03 | -2.0% | 6.97 | -38.5% | ||
| citizenship | 591 | 9.83 | -13.3% | 7.03 | -2.0% | 6.87 | -39.3% |
| economy_employment | 138 | 8.88 | -21.6% | 7.01 | -2.2% | 6.92 | -38.9% |
| wealth_employment | 6878 | 8.79 | -22.4% | 7.01 | -2.2% | 7.06 | -37.6% |
| age | 8052 | 11.1 | -2.3% | 7 | -2.4% | 6.88 | -39.3% |
| transportation | 1803 | 6.98 | -2.6% | 6.87 | -39.4% | ||
| education | 1236 | 8.58 | -24.3% | 6.98 | -2.7% | 6.99 | -38.3% |
| military | 1447 | 10.6 | -6.7% | 6.96 | -2.9% | 6.96 | -38.5% |
| sex | 9968 | 6.93 | -3.4% | 6.88 | -39.3% | ||
| occupation | 4408 | 9.12 | -19.5% | 6.9 | -3.8% | 6.92 | -38.9% |
| global_warming | 60 | 8.23 | -27.4% | 6.83 | -4.8% | 6.88 | -39.3% |
We dissagregate the role of individual features from our best performing model below in Table XXXX. Nearly a quarter of the model’s weight assigned to predictions comes from a single feature, share of Trump Vote in 2020, and it is the only feature that was chosen in all 5 folds. Having Trump share below 50% has a consistent flat increase in vaccine uptake, and then share approaching and surpassing 50% leads to monotonic and near linear decline in uptake. Trump Vote share in 2016 was chosen in 4 out of 5 folds and contributes about another 10% to the model’s predictions, with a similar shape as 2020 vote. Together they account for a third of the model’s weights assigned to out of sample predictions. The next most impactful feature is demographic, percent share African American which accounts for about 4% of model weight. Its contribution is to reduce expected vaccine uptake for counties that are above average African American, monotonically in the share of that population.
| Shap | |||||
|---|---|---|---|---|---|
| Feature | Folds | Sum | % | Cum. % | Marginal Fx. |
| politics Trump Vote 2020 | 5 | 121 | 18.4% | 18.4% |
|
| politics Trump Vote 2016 | 4 | 47.5 | 7.2% | 25.7% |
|
| countyhealthrankings mammography screening | 4 | 10.2 | 1.6% | 27.2% |
|
| nyt masking masking never | 4 | 9.08 | 1.4% | 28.6% |
|
| nyt masking masking always | 3 | 8.65 | 1.3% | 29.9% |
|
| acs age by disability status black or african american alone total≥18 years no disability b1b 004 acs5 | 1 | 8.19 | 1.2% | 31.2% |
|
| acs aggregate household income in the past 12 months in inflation adjusted dollars aggregate household income in the past 12 months in inflation adjusted dollars b5 001 acs5 ita | 1 | 6.59 | 1.0% | 32.2% |
|
| countyhealthrankings adult smoking brfss | 3 | 6.57 | 1.0% | 33.2% |
|
| acs health insurance coverage status by age white alone not hispanic or latino total 19 to 64 years no health insurance coverage c1h 007 acs5 | 2 | 6.25 | 1.0% | 34.1% |
|
| acs health insurance coverage status by age white alone total 19 to 64 years no health insurance coverage c1a 007 acs5 | 2 | 5.73 | 0.9% | 35.0% |
|
| countyhealthrankings dentists | 2 | 5.03 | 0.8% | 35.8% |
|
| acs age of householder by household income in the past 12 months in inflation adjusted dollars total householder 65< 200 000 or more b7 069 acs5 ita | 1 | 4.66 | 0.7% | 36.5% |
|
| nyt masking masking sometimes | 3 | 4.32 | 0.7% | 37.1% |
|
| acs mortgage status by household income in the past 12 months in inflation adjusted dollars total not mortgaged 150 000 or more b8 019 acs5 ita | 3 | 4.26 | 0.6% | 37.8% |
|
| bae gdp in arts, entertainment, and recreation ita | 2 | 4.26 | 0.6% | 38.4% |
|
| acs kitchen facilities for all housing units total complete kitchen facilities b1 002 acs5 ita | 1 | 4.22 | 0.6% | 39.1% |
|
| cew employed NAICS 48-49 Transportation and warehousing Federal Government ita | 2 | 4.11 | 0.6% | 39.7% |
|
| countyhealthrankings adult obesity dss | 3 | 4 | 0.6% | 40.3% |
|
| acs place of birth by age in the united states total born in other state in the united states≥5 years b1 026 acs5 | 2 | 3.94 | 0.6% | 40.9% |
|
| acs age by ratio of income to poverty level in the past 12 months total 65 to 74 years 5 00 and over b4 118 acs5 ita | 2 | 3.89 | 0.6% | 41.5% |
|
| acs mortgage status by value total not mortgaged 500 000 to 749 999 b6 019 acs5 ita | 2 | 3.89 | 0.6% | 42.1% |
|
| acs presence and types of internet subscriptions in household total with an internet subscriptioncellular data plancellular data plan with no other type of internet subscription b2 006 acs5 | 4 | 3.83 | 0.6% | 42.7% |
|
| bae gdp in arts, entertainment, recreation, accommodation, and food services ita | 2 | 3.8 | 0.6% | 43.2% |
|
| acs aggregate income in the past 12 months in inflation adjusted dollars aggregate income in the past 12 months in inflation adjusted dollars b3 001 acs5 ita | 1 | 3.62 | 0.6% | 43.8% |
|
| acs health insurance coverage status and type by household income in the past 12 months in inflation adjusted dollars total≥25 000 no health insurance coverage b5 006 acs5 ita | 3 | 3.6 | 0.5% | 44.3% |
|
| nyt masking masking rarely | 2 | 3.59 | 0.5% | 44.9% |
|
| acs tenure by vehicles available total owner occupied 2 vehicles available b4 005 acs5 | 2 | 3.42 | 0.5% | 45.4% |
|
| acs geographical mobility in the past year by individual income in the past 12 months in inflation adjusted dollars for current residence in the united states total same house 1 year ago with income 35 000 to 49 999 b0 019 acs5 ita | 1 | 3.4 | 0.5% | 45.9% |
|
| acs people reporting single ancestry total eastern european b4 035 acs5 | 2 | 3.4 | 0.5% | 46.4% |
|
| therda united methodist church, the ita | 1 | 3.3 | 0.5% | 47.0% |
|
| acs types of health insurance coverage by age total 19 to 34 years no health insurance coverage b0 033 acs5 | 2 | 3.22 | 0.5% | 47.4% |
|
| acs mortgage status by aggregate real estate taxes paid dollars aggregate real estate taxes paid dollars b0 001 acs5 ita | 1 | 3.06 | 0.5% | 47.9% |
|
| countyhealthrankings poor or fair health brfss | 2 | 3.04 | 0.5% | 48.4% |
|
| acs place of birth by age in the united states total born in state of residence 75< b1 024 acs5 | 3 | 3.03 | 0.5% | 48.8% |
|
| acs public health insurance by ratio of income to poverty level in the past 12 months by age total 1 00 to 1 37 of poverty threshold 19 to 64 years no public coverage c8 018 acs5 ita | 2 | 2.94 | 0.4% | 49.3% |
|
| acs tenure by units in structure total renter occupied housing units 1 detached b2 014 acs5 ita | 1 | 2.94 | 0.4% | 49.7% |
|
| cew employed NAICS 92 Public administration Local Government ita | 3 | 2.93 | 0.4% | 50.2% |
|
| countyhealthrankings flu vaccinations medicare | 2 | 2.9 | 0.4% | 50.6% |
|
| acs mortgage status by real estate taxes paid total not mortgaged no real estate taxes paid b2 015 acs5 | 2 | 2.74 | 0.4% | 51.0% |
|
| acs mortgage status by real estate taxes paid total not mortgaged 3 000 or more b2 014 acs5 | 2 | 2.7 | 0.4% | 51.4% |
|
| therda evangelical protestant ita | 4 | 2.67 | 0.4% | 51.8% |
|
| acs aggregate value dollars by age of householder aggregate value dollars householder 65< dollars b9 005 acs5 ita | 3 | 2.65 | 0.4% | 52.2% |
|
| acs age by disability status black or african american alone total 18 to 64 years with a disability b1b 006 acs5 | 1 | 2.6 | 0.4% | 52.6% |
|
| acs aggregate nonfamily household income in the past 12 months in inflation adjusted dollars aggregate nonfamily household income in the past 12 months in inflation adjusted dollars b4 001 acs5 ita | 1 | 2.59 | 0.4% | 53.0% |
|
| acs internet subscriptions in household total with an internet subscriptionbroadband such as cable fiber optic or dsl b1 004 acs5 | 2 | 2.57 | 0.4% | 53.4% |
|
| therda christian churches and churches of christ ita | 3 | 2.46 | 0.4% | 53.8% |
|
| acs year structure built total built to b4 005 acs5 ita | 1 | 2.41 | 0.4% | 54.2% |
|
| politics georgewbush | 4 | 2.39 | 0.4% | 54.5% |
|
| acs place of birth by marital status in the united states total foreign born separated b8 029 acs5 | 1 | 2.38 | 0.4% | 54.9% |
|
| acs rooms total 2 rooms b7 003 acs5 ita | 3 | 2.31 | 0.4% | 55.3% |
|
| bae gdp in real estate and rental and leasing ita | 2 | 2.31 | 0.4% | 55.6% |
|
| google vaccine facilities count ita | 3 | 2.31 | 0.4% | 56.0% |
|
| cew employed NAICS 44-45 Retail trade Private ita | 3 | 2.29 | 0.3% | 56.3% |
|
| acs income in the past 12 months in inflation adjusted dollars white alone not hispanic or latino income in the past 12 months in inflation adjusted dollars b1h 001 acs5 | 1 | 2.18 | 0.3% | 56.6% |
|
| countyhealthrankings flu vaccinations white medicare | 2 | 2.16 | 0.3% | 57.0% |
|
| acs family type by presence of own children≥18 years by family income in the past 12 months in inflation adjusted dollars total married couple family no own children of the householder≥18 years 200 000 or more b1 036 acs5 ita | 1 | 2.13 | 0.3% | 57.3% |
|
| acs public health insurance by ratio of income to poverty level in the past 12 months by age total 1 38 to 1 99 of poverty threshold 19 to 64 years with public coverage c8 027 acs5 ita | 1 | 2.1 | 0.3% | 57.6% |
|
| acs geographical mobility in the past year by age for residence 1 year ago in the united states total living in area 1 year ago 45 to 49 years b1 010 acs5 | 2 | 2.1 | 0.3% | 57.9% |
|
| countyhealthrankings preventable hospital stays white | 2 | 2.08 | 0.3% | 58.2% |
|
| politics georgewbush | 4 | 2.05 | 0.3% | 58.6% |
|
| acs age and nativity of own children≥18 years in families and subfamilies by number and nativity of parents total 6 to 17 years living with two parents both parents native b9 024 acs5 | 2 | 2.04 | 0.3% | 58.9% |
|
| acs poverty status in the past 12 months by age white alone not hispanic or latino total income in the past 12 months at or above poverty level 85< b0h 017 acs5 ita | 1 | 2.03 | 0.3% | 59.2% |
|
| pop aian ita | 2 | 2.02 | 0.3% | 59.5% |
|
| acs geographical mobility in the past year by individual income in the past 12 months in inflation adjusted dollars for residence 1 year ago in the united states total living in area 1 year ago with income 35 000 to 49 999 b0 008 acs5 ita | 1 | 2.02 | 0.3% | 59.8% |
|
| countyhealthrankings teen births white | 2 | 1.97 | 0.3% | 60.1% |
|
| acs grandparents living with own grandchildren≥18 years by responsibility for own grandchildren by length of time responsible for own grandchildren for the population 30< total living with own grandchildren≥18 years grandparent responsible for own grandchildren≥18 years grandparent responsible 1 or 2 years b0 006 acs5 | 1 | 1.97 | 0.3% | 60.4% |
|
| acs geographical mobility in the past year by individual income in the past 12 months in inflation adjusted dollars for residence 1 year ago in the united states total living in area 1 year ago b0 001 acs5 ita | 1 | 1.95 | 0.3% | 60.7% |
|
| acs health insurance coverage status and type by household income in the past 12 months in inflation adjusted dollars total 75 000 to 99 999 with health insurance coverage b5 018 acs5 ita | 1 | 1.93 | 0.3% | 61.0% |
|
| safegraph population flows external total ita | 3 | 1.92 | 0.3% | 61.3% |
|
| acs value total 35 000 to 39 999 b5 008 acs5 | 2 | 1.92 | 0.3% | 61.6% |
|
| acs house heating fuel total wood b0 007 acs5 | 2 | 1.92 | 0.3% | 61.9% |
|
| acs tenure by year householder moved into unit by units in structure total renter occupied moved in or later 5 to 19 b9 042 acs5 ita | 1 | 1.9 | 0.3% | 62.1% |
|
| acs geographical mobility in the past year by individual income in the past 12 months in inflation adjusted dollars for current residence in the united states total same house 1 year ago with income 50 000 to 64 999 b0 020 acs5 ita | 1 | 1.88 | 0.3% | 62.4% |
|
| acs types of health insurance coverage by age total≥19 years with one type of health insurance coverage with direct purchase health insurance only b0 005 acs5 | 1 | 1.88 | 0.3% | 62.7% |
|
| acs allocation of age total not allocated b2 003 acs5 | 2 | 1.87 | 0.3% | 63.0% |
|
| acs health insurance coverage status by age white alone not hispanic or latino total≥19 years no health insurance coverage c1h 004 acs5 | 3 | 1.86 | 0.3% | 63.3% |
|
| acs bedrooms total no bedroom b1 002 acs5 ita | 4 | 1.8 | 0.3% | 63.6% |
|
| acs place of birth by nativity and citizenship status total native born in other state in the united states midwest b2 006 acs5 | 2 | 1.79 | 0.3% | 63.8% |
|
| countyhealthrankings preventable hospital stays | 2 | 1.78 | 0.3% | 64.1% |
|
| acs tenure by age of householder total owner occupied householder 85< b7 011 acs5 | 2 | 1.77 | 0.3% | 64.4% |
|
| acs aggregate income in the past 12 months in inflation adjusted dollars for the population 15< white alone not hispanic or latino aggregate income in the past 12 months in inflation adjusted dollars b3h 001 acs5 ita | 1 | 1.77 | 0.3% | 64.6% |
|
| therda christian methodist episcopal church--rates of adherence per 1,000 population ita | 3 | 1.75 | 0.3% | 64.9% |
|
| acs tenure by household size by units in structure total owner occupied 2 person household 5 to 19 b4 013 acs5 ita | 1 | 1.75 | 0.3% | 65.2% |
|
| acs poverty status in the past 12 months of families by household type by number of own children≥18 years total income in the past 12 months at or above poverty level married couple family 5 or more own children of the householder b3 024 acs5 ita | 1 | 1.73 | 0.3% | 65.4% |
|
| acs household size by vehicles available total 4 or more person household 4 or more vehicles available b1 030 acs5 | 2 | 1.73 | 0.3% | 65.7% |
|
| acs tenure by vehicles available total owner occupied 1 vehicle available b4 004 acs5 | 2 | 1.71 | 0.3% | 66.0% |
|
| vaccinedotgov supply locations ita | 1 | 1.7 | 0.3% | 66.2% |
|
| cew change employed NAICS 92 Public administration Local Government | 2 | 1.66 | 0.3% | 66.5% |
|
| acs poverty status in the past 12 months by age total income in the past 12 months at or above poverty level 85< b0 017 acs5 ita | 1 | 1.65 | 0.3% | 66.7% |
|
| acs people reporting ancestry total eastern european b6 035 acs5 | 2 | 1.65 | 0.3% | 67.0% |
|
| acs people reporting ancestry total lithuanian b6 053 acs5 | 2 | 1.65 | 0.3% | 67.2% |
|
| acs age of householder by household income in the past 12 months in inflation adjusted dollars american indian and alaska native alone householder total householder 45 to 64 years 35 000 to 39 999 b7c 043 acs5 ita | 1 | 1.62 | 0.2% | 67.5% |
|
| acs rooms total 1 room b7 002 acs5 | 3 | 1.61 | 0.2% | 67.7% |
|
| acs public health insurance by ratio of income to poverty level in the past 12 months by age total 4 00 of poverty threshold and over 65< with public coverage c8 050 acs5 ita | 1 | 1.6 | 0.2% | 68.0% |
|
| bae gdp in private services-providing industries 3/ ita | 1 | 1.59 | 0.2% | 68.2% |
|
| acs health insurance coverage status and type by citizenship status total native born no health insurance coverage b0 006 acs5 | 1 | 1.53 | 0.2% | 68.4% |
|
| cew change employed NAICS 92 Public administration State Government | 2 | 1.51 | 0.2% | 68.7% |
|
| bae gdp in educational services, health care, and social assistance | 2 | 1.49 | 0.2% | 68.9% |
|
| vap aian ita | 2 | 1.47 | 0.2% | 69.1% |
|
| bae gdp in government and government enterprises ita | 2 | 1.45 | 0.2% | 69.3% |
|
| google grocery and pharmacy change from baseline | 3 | 1.44 | 0.2% | 69.6% |
|
| therda african methodist episcopal church--rates of adherence per 1,000 population ita | 1 | 1.43 | 0.2% | 69.8% |
|
| bae gdp in information ita | 2 | 1.42 | 0.2% | 70.0% |
|
| bae gdp in professional and business services ita | 1 | 1.42 | 0.2% | 70.2% |
|
| politics johnmccain | 3 | 1.42 | 0.2% | 70.4% |
|
| acs population≥18 years by age total in households 15 to 17 years b1 009 acs5 | 1 | 1.41 | 0.2% | 70.6% |
|
| acs mortgage status by real estate taxes paid total with a mortgage 2 000 to 2 999 b2 006 acs5 | 1 | 1.4 | 0.2% | 70.9% |
|
| covid19projections total infected mean ita | 1 | 1.39 | 0.2% | 71.1% |
|
| acs age by language spoken at home by ability to speak english for the population 5< total 5 to 17 years speak only english b4 003 acs5 | 2 | 1.39 | 0.2% | 71.3% |
|
| acs age by language spoken at home for the population 5< total 65< speak other indo european languages b7 017 acs5 | 1 | 1.38 | 0.2% | 71.5% |
|
| acs allocation of citizenship status total native allocated b1 003 acs5 | 3 | 1.38 | 0.2% | 71.7% |
|
| therda catholic church ita | 1 | 1.38 | 0.2% | 71.9% |
|
| acs aggregate public assistance income in the past 12 months in inflation adjusted dollars for households aggregate public assistance income in the past 12 months in inflation adjusted dollars b7 001 acs5 ita | 1 | 1.37 | 0.2% | 72.1% |
|
| acs health insurance coverage status and type by household income in the past 12 months in inflation adjusted dollars total 100 000 or more with health insurance coveragewith public coverage b5 025 acs5 ita | 2 | 1.37 | 0.2% | 72.3% |
|
| acs private health insurance by ratio of income to poverty level in the past 12 months by age total 1 00 to 1 37 of poverty threshold 19 to 64 years with private health insurance c7 017 acs5 ita | 1 | 1.37 | 0.2% | 72.5% |
|
| acs nativity by language spoken at home by ability to speak english for the population 5< total native speak spanish speak english well b5 006 acs5 | 1 | 1.37 | 0.2% | 72.7% |
|
| acs tenure by year structure built total owner occupied built to b6 007 acs5 ita | 1 | 1.36 | 0.2% | 73.0% |
|
| acs geographical mobility in the past year black or african american alone for current residence in the united states total moved from different county within same state b4b 004 acs5 | 1 | 1.36 | 0.2% | 73.2% |
|
| bae gdp in professional and business services | 1 | 1.35 | 0.2% | 73.4% |
|
| bae gdp in other services ita | 2 | 1.32 | 0.2% | 73.6% |
|
| therda population in ita | 1 | 1.32 | 0.2% | 73.8% |
|
| acs age by ratio of income to poverty level in the past 12 months total 45 to 54 years b4 080 acs5 ita | 1 | 1.3 | 0.2% | 74.0% |
|
| acs allocation of bedrooms total allocated b8 002 acs5 ita | 2 | 1.27 | 0.2% | 74.2% |
|
| acs tenure by age of householder by units in structure total owner occupied householder 65< 5 to 19 b5 020 acs5 ita | 1 | 1.23 | 0.2% | 74.3% |
|
| acs subfamily type by presence of own children≥18 years total married couple subfamily with own children≥18 years b3 003 acs5 | 1 | 1.23 | 0.2% | 74.5% |
|
| cew change employed NAICS 44-45 Retail trade Private | 2 | 1.22 | 0.2% | 74.7% |
|
| cew employed NAICS 31-33 Manufacturing Private | 2 | 1.22 | 0.2% | 74.9% |
|
| acs allocation of household computer type total smartphone allocated b2 004 acs5 | 1 | 1.2 | 0.2% | 75.1% |
|
| acs people reporting single ancestry total polish b4 061 acs5 | 3 | 1.19 | 0.2% | 75.3% |
|
| acs aggregate household income in the past 12 months in inflation adjusted dollars white alone householder aggregate household income in the past 12 months in inflation adjusted dollars b5a 001 acs5 ita | 1 | 1.19 | 0.2% | 75.5% |
|
| acs allocation of relationship total allocated b2 002 acs5 | 1 | 1.18 | 0.2% | 75.6% |
|
| acs hispanic or latino origin by specific origin total hispanic or latino other hispanic or latino b1 027 acs5 | 1 | 1.17 | 0.2% | 75.8% |
|
| acs tenure by household size by units in structure total renter occupied 5 or more person household mobile home boat rv van etc b4 073 acs5 ita | 2 | 1.17 | 0.2% | 76.0% |
|
| acs poverty status in the past 12 months by household type by age of householder total income in the past 12 months at or above poverty level b7 031 acs5 ita | 1 | 1.16 | 0.2% | 76.2% |
|
| acs households by presence of people 60< by household type total households with no people 60< family households other family b6 012 acs5 | 1 | 1.16 | 0.2% | 76.3% |
|
| acs geographical mobility in the past year by age for current residence in the united states total 45 to 49 years b1 010 acs5 | 1 | 1.15 | 0.2% | 76.5% |
|
| acs tenure by plumbing facilities by occupants per room total owner occupied complete plumbing facilities 1 51 or more occupants per room b6 006 acs5 ita | 1 | 1.14 | 0.2% | 76.7% |
|
| acs geographical mobility in the past year by tenure for current residence in the united states total moved from different state householder lived in owner occupied housing units b3 014 acs5 | 1 | 1.14 | 0.2% | 76.9% |
|
| acs language and ability to speak english of grandparents living with own grandchildren≥18 years by responsibility for own grandchildren and age of grandparent total speak only english b4 002 acs5 | 2 | 1.12 | 0.2% | 77.0% |
|
| acs tenure by year householder moved into unit by units in structure total owner occupied moved in or earlier 1 detached or attached b9 032 acs5 ita | 1 | 1.11 | 0.2% | 77.2% |
|
| countyhealthrankings mental health providers | 2 | 1.1 | 0.2% | 77.4% |
|
| acs types of health insurance coverage by age total 35 to 64 years with two or more types of health insurance coverage b0 042 acs5 | 2 | 1.09 | 0.2% | 77.5% |
|
| acs grandparents living with own grandchildren≥18 years by responsibility for own grandchildren by length of time responsible for own grandchildren for the population 30< total not living with own grandchildren≥18 years b0 010 acs5 | 1 | 1.08 | 0.2% | 77.7% |
|
| therda catholic--total number of congregations ita | 1 | 1.08 | 0.2% | 77.9% |
|
| acs allocation of hispanic or latino origin total not allocated b1 003 acs5 | 1 | 1.07 | 0.2% | 78.0% |
|
| acs place of birth by individual income in the past 12 months in inflation adjusted dollars in the united states total born in state of residence with income 75 000 or more b0 022 acs5 ita | 1 | 1.07 | 0.2% | 78.2% |
|
| acs people reporting multiple ancestry total polish b5 061 acs5 | 1 | 1.06 | 0.2% | 78.4% |
|
| acs household size by vehicles available total 2 person household 2 vehicles available b1 016 acs5 | 1 | 1.02 | 0.2% | 78.5% |
|
| acs own children≥18 years by family type and age total b2 001 acs5 | 1 | 1.02 | 0.2% | 78.7% |
|
| acs people reporting ancestry total scotch irish b6 066 acs5 | 2 | 0.978 | 0.1% | 78.8% |
|
| acs receipt of food stamps snap in the past 12 months by race of householder hispanic or latino total household received food stamps snap in the past 12 months b5i 002 acs5 | 1 | 0.968 | 0.1% | 79.0% |
|
| acs age by disability status by health insurance coverage status total 19 to 64 years with a disability with health insurance coverage with private health insurance coverage b5 016 acs5 | 2 | 0.953 | 0.1% | 79.1% |
|
| acs tenure by year householder moved into unit by units in structure total owner occupied moved in or later mobile home boat rv van etc b9 009 acs5 ita | 1 | 0.934 | 0.1% | 79.2% |
|
| acs language spoken at home by ability to speak english for the population 5< hispanic or latino total speak only english b6 002 acs5 | 1 | 0.927 | 0.1% | 79.4% |
|
| acs household size by vehicles available total 3 person household 3 vehicles available b1 023 acs5 | 1 | 0.901 | 0.1% | 79.5% |
|
| acs people reporting multiple ancestry total french except basque b5 040 acs5 | 2 | 0.898 | 0.1% | 79.7% |
|
| cew employed NAICS 99 Unclassified Private ita | 1 | 0.893 | 0.1% | 79.8% |
|
| acs age of householder by household income in the past 12 months in inflation adjusted dollars total householder 25 to 44 years 20 000 to 24 999 b7 023 acs5 ita | 1 | 0.886 | 0.1% | 79.9% |
|
| acs geographical mobility in the past year by individual income in the past 12 months in inflation adjusted dollars for residence 1 year ago in the united states total living in area 1 year ago moved to different state no income b0 046 acs5 ita | 1 | 0.882 | 0.1% | 80.1% |
|
| pop other ita | 1 | 0.882 | 0.1% | 80.2% |
|
| acs health insurance coverage status by age white alone total≥19 years no health insurance coverage c1a 004 acs5 | 1 | 0.878 | 0.1% | 80.3% |
|
| acs units in structure total 20 to 49 b4 008 acs5 ita | 1 | 0.874 | 0.1% | 80.5% |
|
| acs allocation of hispanic or latino origin total allocated b1 002 acs5 | 1 | 0.873 | 0.1% | 80.6% |
|
| acs geographical mobility in the past year by age for residence 1 year ago in the united states total living in area 1 year ago moved to different state 18 and 19 years b1 068 acs5 | 1 | 0.866 | 0.1% | 80.7% |
|
| acs presence of a computer and type of internet subscription in household total has a computer with a broadband subscription cellular data plan alone or with dial up b8 008 acs5 | 1 | 0.862 | 0.1% | 80.9% |
|
| bae gdp in health care and social assistance | 1 | 0.859 | 0.1% | 81.0% |
|
| acs tenure by vehicles available total renter occupied 4 vehicles available b4 014 acs5 | 1 | 0.858 | 0.1% | 81.1% |
|
| acs mortgage status by real estate taxes paid total with a mortgage no real estate taxes paid b2 008 acs5 | 2 | 0.852 | 0.1% | 81.3% |
|
| acs allocation of mortgage status total allocated b21 002 acs5 | 2 | 0.846 | 0.1% | 81.4% |
|
| acs aggregate household income in the past 12 months in inflation adjusted dollars by age of householder aggregate household income in the past 12 months in inflation adjusted dollars householder 65< b0 005 acs5 ita | 1 | 0.843 | 0.1% | 81.5% |
|
| acs population≥18 years by age total in households 9 to 11 years b1 007 acs5 | 1 | 0.84 | 0.1% | 81.6% |
|
| acs types of health insurance coverage by age total 35 to 64 years with one type of health insurance coverage with medicaid means tested public coverage only b0 039 acs5 | 1 | 0.836 | 0.1% | 81.8% |
|
| acs aggregate value dollars by year structure built aggregate value dollars built to b8 006 acs5 ita | 1 | 0.835 | 0.1% | 81.9% |
|
| acs language and ability to speak english of grandparents living with own grandchildren≥18 years by responsibility for own grandchildren and age of grandparent total speak other language speak english less than very well grandparent responsible for own grandchildren≥18 years b4 014 acs5 | 1 | 0.835 | 0.1% | 82.0% |
|
| acs geographical mobility in the past year by individual income in the past 12 months in inflation adjusted dollars for current residence in the united states total moved within same county with income 35 000 to 49 999 b0 030 acs5 ita | 1 | 0.833 | 0.1% | 82.1% |
|
| cew employed NAICS 52 Finance and insurance Private ita | 1 | 0.831 | 0.1% | 82.3% |
|
| acs household size by vehicles available total 4 or more vehicles available b1 006 acs5 | 2 | 0.825 | 0.1% | 82.4% |
|
| countyhealthrankings uninsured sahi | 1 | 0.819 | 0.1% | 82.5% |
|
| acs public health insurance by ratio of income to poverty level in the past 12 months by age total 1 00 to 1 37 of poverty threshold≥19 years no public coverage c8 015 acs5 ita | 2 | 0.814 | 0.1% | 82.6% |
|
| cew employed NAICS 92 Public administration State Government ita | 1 | 0.808 | 0.1% | 82.8% |
|
| acs people reporting single ancestry total american b4 005 acs5 | 1 | 0.806 | 0.1% | 82.9% |
|
| therda black protestant--rates of adherence per 1,000 population ita | 1 | 0.803 | 0.1% | 83.0% |
|
| acs aggregate family income in the past 12 months in inflation adjusted dollars by family type by presence of own children≥18 years aggregate family income in the past 12 months in inflation adjusted dollars other family b8 005 acs5 ita | 1 | 0.801 | 0.1% | 83.1% |
|
| acs tenure by year structure built by units in structure total owner occupied built to mobile home boat rv van etc b7 030 acs5 ita | 1 | 0.8 | 0.1% | 83.3% |
|
| cew employed NAICS 31-33 Manufacturing Private ita | 1 | 0.797 | 0.1% | 83.4% |
|
| acs receipt of food stamps snap in the past 12 months by disability status for households total household did not receive food stamps snap in the past 12 months households with no persons with a disability b0 007 acs5 | 1 | 0.796 | 0.1% | 83.5% |
|
| acs age of householder by household income in the past 12 months in inflation adjusted dollars white alone householder total householder 25 to 44 years 100 000 to 124 999 b7a 032 acs5 ita | 1 | 0.788 | 0.1% | 83.6% |
|
| acs geographical mobility in the past year by tenure for residence 1 year ago in the united states total living in area 1 year ago b3 001 acs5 | 1 | 0.783 | 0.1% | 83.7% |
|
| acs age by ratio of income to poverty level in the past 12 months total 55 to 64 years 1 85 to 1 99 b4 101 acs5 ita | 1 | 0.782 | 0.1% | 83.9% |
|
| acs age by language spoken at home by ability to speak english for the population 5< total 65< speak other indo european languages speak english very well b4 054 acs5 | 1 | 0.772 | 0.1% | 84.0% |
|
| acs geographical mobility in the past year by age for current residence in the united states total moved from different county within same state 5 to 17 years b1 051 acs5 | 1 | 0.765 | 0.1% | 84.1% |
|
| acs poverty status in the past 12 months of families by household type by number of persons in family total income in the past 12 months at or above poverty level married couple family 7 or more people b3 024 acs5 ita | 1 | 0.764 | 0.1% | 84.2% |
|
| acs place of birth by age in the united states total born in state of residence 65 to 74 years b1 023 acs5 | 1 | 0.763 | 0.1% | 84.3% |
|
| acs tenure by year structure built by units in structure total renter occupied built to 1 detached or attached b7 068 acs5 ita | 1 | 0.759 | 0.1% | 84.4% |
|
| acs public health insurance by ratio of income to poverty level in the past 12 months by age total 1 38 to 1 99 of poverty threshold 19 to 64 years no public coverage c8 028 acs5 ita | 1 | 0.757 | 0.1% | 84.6% |
|
| bae gdp in manufacturing ita | 1 | 0.748 | 0.1% | 84.7% |
|
| acs age of householder by household income in the past 12 months in inflation adjusted dollars total householder 45 to 64 years 10 000 to 14 999 b7 038 acs5 ita | 1 | 0.739 | 0.1% | 84.8% |
|
| acs age by ratio of income to poverty level in the past 12 months total≥6 years 75 to 99 b4 005 acs5 ita | 1 | 0.738 | 0.1% | 84.9% |
|
| acs tenure by rooms total renter occupied 6 rooms b0 018 acs5 ita | 1 | 0.735 | 0.1% | 85.0% |
|
| acs presence of a computer and type of internet subscription in household some other race alone total has a computer without an internet subscription b9f 005 acs5 | 1 | 0.73 | 0.1% | 85.1% |
|
| acs age of householder by household income in the past 12 months in inflation adjusted dollars white alone householder total householder 65< 150 000 to 199 999 b7a 068 acs5 ita | 1 | 0.727 | 0.1% | 85.2% |
|
| acs tenure by household type including living alone and age of householder total renter occupied family households married couple family b1 028 acs5 | 1 | 0.718 | 0.1% | 85.3% |
|
| acs place of birth by nativity and citizenship status total native born in other state in the united states northeast b2 005 acs5 | 1 | 0.716 | 0.1% | 85.4% |
|
| acs household size by vehicles available total 2 person household 4 or more vehicles available b1 018 acs5 | 1 | 0.711 | 0.1% | 85.6% |
|
| acs gross rent total with cash rent 400 to 449 b3 010 acs5 | 1 | 0.706 | 0.1% | 85.7% |
|
| therda non-denominational--total number of congregations ita | 2 | 0.703 | 0.1% | 85.8% |
|
| acs tenure by rooms total renter occupied 7 rooms b0 019 acs5 ita | 1 | 0.696 | 0.1% | 85.9% |
|
| acs tenure by vehicles available total renter occupied 5 or more vehicles available b4 015 acs5 | 1 | 0.693 | 0.1% | 86.0% |
|
| acs mortgage status by value total with a mortgage less than 50 000 b6 003 acs5 ita | 1 | 0.681 | 0.1% | 86.1% |
|
| acs income in the past 12 months in inflation adjusted dollars hispanic or latino income in the past 12 months in inflation adjusted dollars b1i 001 acs5 | 1 | 0.675 | 0.1% | 86.2% |
|
| acs age by ratio of income to poverty level in the past 12 months total 45 to 54 years 2 00 to 2 99 b4 089 acs5 ita | 1 | 0.668 | 0.1% | 86.3% |
|
| acs allocation of earnings in the past 12 months for the population 16< earnings allocated earnings allocated no earnings allocated b1 002 acs5 | 2 | 0.668 | 0.1% | 86.4% |
|
| acs american indian and alaska native alone for selected tribal groupings total american indian tribes specified apache b4 003 acs5 | 1 | 0.667 | 0.1% | 86.5% |
|
| acs language spoken at home for the population 5< total spanish speak english very well c1 004 acs5 | 1 | 0.662 | 0.1% | 86.6% |
|
| acs place of birth by individual income in the past 12 months in inflation adjusted dollars in the united states total with income 35 000 to 49 999 b0 008 acs5 ita | 1 | 0.66 | 0.1% | 86.7% |
|
| acs age by ratio of income to poverty level in the past 12 months total 25 to 34 years 1 00 to 1 24 b4 058 acs5 ita | 1 | 0.657 | 0.1% | 86.8% |
|
| acs poverty status in the past 12 months by household type by age of householder total income in the past 12 months below poverty level family households married couple family householder 45 to 64 years b7 007 acs5 ita | 2 | 0.654 | 0.1% | 86.9% |
|
| acs contract rent total with cash rent 350 to 399 b6 009 acs5 | 1 | 0.654 | 0.1% | 87.0% |
|
| acs geographical mobility in the past year by age for residence 1 year ago in the united states total living in area 1 year ago moved to different county within same state 40 to 44 years b1 057 acs5 | 1 | 0.653 | 0.1% | 87.1% |
|
| acs tenure by year structure built total renter occupied built to b6 022 acs5 ita | 1 | 0.648 | 0.1% | 87.2% |
|
| acs people reporting multiple ancestry total lithuanian b5 053 acs5 | 2 | 0.642 | 0.1% | 87.3% |
|
| acs year structure built total built to b4 008 acs5 ita | 2 | 0.636 | 0.1% | 87.4% |
|
| acs allocation of public health insurance total b03 001 acs5 | 1 | 0.629 | 0.1% | 87.5% |
|
| acs aggregate price asked dollars aggregate price asked dollars b6 001 acs5 ita | 1 | 0.622 | 0.1% | 87.6% |
|
| acs mortgage status by real estate taxes paid total with a mortgage 3 000 or more b2 007 acs5 | 1 | 0.62 | 0.1% | 87.7% |
|
| acs place of birth by individual income in the past 12 months in inflation adjusted dollars in the united states total native born outside the united states with income 75 000 or more b0 044 acs5 ita | 1 | 0.62 | 0.1% | 87.8% |
|
| acs aggregate value dollars by age of householder aggregate value dollars b9 001 acs5 ita | 1 | 0.619 | 0.1% | 87.9% |
|
| bae gdp in professional, scientific, and technical services | 2 | 0.614 | 0.1% | 88.0% |
|
| acs geographical mobility in the past year by marital status for residence 1 year ago in the united states total living in area 1 year ago b8 001 acs5 | 1 | 0.604 | 0.1% | 88.0% |
|
| acs people reporting single ancestry total danish b4 033 acs5 | 2 | 0.599 | 0.1% | 88.1% |
|
| bae gdp in all industry total ita | 1 | 0.598 | 0.1% | 88.2% |
|
| acs poverty status in the past 12 months of families by household type by number of related children≥18 years total income in the past 12 months at or above poverty level married couple family 5 or more children b2 024 acs5 ita | 1 | 0.597 | 0.1% | 88.3% |
|
| acs age by ratio of income to poverty level in the past 12 months total 75< 5 00 and over b4 131 acs5 ita | 1 | 0.592 | 0.1% | 88.4% |
|
| acs allocation of food stamps snap receipt total allocated b1 002 acs5 | 2 | 0.591 | 0.1% | 88.5% |
|
| acs allocation of residence 1 year ago for the population 1 year and over total different house allocated one or more but not all geographic parts allocated b2 004 acs5 | 1 | 0.59 | 0.1% | 88.6% |
|
| acs health insurance coverage status by ratio of income to poverty level in the past 12 months by age total 1 00 to 1 37 of poverty threshold 19 to 64 years no health insurance coverage c6 018 acs5 ita | 1 | 0.582 | 0.1% | 88.7% |
|
| acs median gross rent by year structure built median gross rent total built to b1 003 acs5 ita | 1 | 0.582 | 0.1% | 88.8% |
|
| acs tenure by age of householder by year structure built total owner occupied householder 65< built to b6 030 acs5 ita | 2 | 0.577 | 0.1% | 88.9% |
|
| safegraph population flows self total ita | 1 | 0.576 | 0.1% | 88.9% |
|
| acs geographical mobility in the past year by individual income in the past 12 months in inflation adjusted dollars for residence 1 year ago in the united states total living in area 1 year ago moved to different state with income 25 000 to 34 999 b0 051 acs5 ita | 1 | 0.565 | 0.1% | 89.0% |
|
| acs allocation of language spoken at home for the population 5< total speak other languages specific languages spoken allocated language status not allocated b2 006 acs5 | 1 | 0.564 | 0.1% | 89.1% |
|
| acs population in subfamilies by subfamily type by relationship total b4 001 acs5 | 1 | 0.557 | 0.1% | 89.2% |
|
| acs poverty status in the past 12 months by age white alone total income in the past 12 months at or above poverty level 85< b0a 017 acs5 ita | 1 | 0.557 | 0.1% | 89.3% |
|
| acs family type by presence and age of related children≥18 years total married couple family with related children of the householder≥18 years≥6 years and 6 to 17 years b4 005 acs5 | 1 | 0.551 | 0.1% | 89.4% |
|
| acs household size by vehicles available total 1 person household 1 vehicle available b1 009 acs5 | 2 | 0.547 | 0.1% | 89.4% |
|
| acs household income in the past 12 months in inflation adjusted dollars by value total household income the past 12 months in inflation adjusted dollars 35 000 to 49 999 value 20 000 to 29 999 b1 050 acs5 ita | 1 | 0.54 | 0.1% | 89.5% |
|
| bae gdp in utilities ita | 1 | 0.539 | 0.1% | 89.6% |
|
| acs age by ratio of income to poverty level in the past 12 months total 25 to 34 years 1 75 to 1 84 b4 061 acs5 ita | 1 | 0.53 | 0.1% | 89.7% |
|
| cew employed NAICS 81 Other services, except public administration Private | 1 | 0.529 | 0.1% | 89.8% |
|
| acs kitchen facilities by meals included in rent total complete kitchen facilities meals included in rent b4 003 acs5 ita | 1 | 0.528 | 0.1% | 89.9% |
|
| acs language and ability to speak english of grandparents living with own grandchildren≥18 years by responsibility for own grandchildren and age of grandparent total speak other language speak english very well grandparent responsible for own grandchildren≥18 years b4 009 acs5 | 1 | 0.523 | 0.1% | 89.9% |
|
| acs poverty status in the past 12 months by household type by age of householder total income in the past 12 months at or above poverty level family households married couple family householder≥25 years b7 034 acs5 ita | 1 | 0.522 | 0.1% | 90.0% |
|
| acs units in structure total 3 or 4 b4 005 acs5 ita | 1 | 0.521 | 0.1% | 90.1% |
|
| acs household income in the past 12 months in inflation adjusted dollars by gross rent total household income in the past 12 months in inflation adjusted dollars less than 10 000 with cash rent 600 to 699 b2 010 acs5 ita | 1 | 0.52 | 0.1% | 90.2% |
|
| politics mittromney | 2 | 0.517 | 0.1% | 90.3% |
|
| bae gdp in transportation and warehousing ita | 1 | 0.514 | 0.1% | 90.3% |
|
| cew employed NAICS 92 Public administration Federal Government | 1 | 0.51 | 0.1% | 90.4% |
|
| vaccinedotgov supply locations insurance ita | 1 | 0.507 | 0.1% | 90.5% |
|
| acs relationship by household type including living alone for the population 65< total in group quarters b0 021 acs5 | 1 | 0.504 | 0.1% | 90.6% |
|
References
We hierarchically cluster interstate trips using Ward’s distance, and cut the dendrogram at 6 clusters. New England presents as a unique cluster but has only 45 counties and so we collapse it the North East folds resulting in 5 total.↩︎